Abstract：As a non-destructive detection method, near-infrared (NIR) spectra have been widely used in food analysis. In NIR analysis, the model between spectra and sample concentrations should be calibrated in advance, and the concentrations of new samples can be predicted by substituting their spectra with the calibrated model. However, the variation of measurement conditions can lead to spectra changes. This problem can be solved by calibration transfer which corrects the new spectra (secondary spectra) to be accurately predicted by the old spectra (primary spectra) model. The calibration transfer always uses full primary and secondary spectra for correction. However, full primary and secondary spectra contain interference, including noise and background, which can increase prediction errors. Hence, variable select is used to selecting the informative regions of NIR for calibration transfer. The commonly used variable selection method always treats primary spectra, and both primary and secondary spectra share the same regions for calibration transfer. However, in practical work, the informative regions of primary and secondary spectra are not the same. Thus, both primary and secondary spectra using the same regions for calibration transfer can increase prediction errors. Moreover, the original spectral ranges of primary and secondary spectra may not be the same, and the secondary spectra can not use the regions selected by primary spectra for calibration transfer. In order to solve this problem, this paper proposed a Genetic algorithm for intervals of double spectra (GA-IDS), which selects informative regions for both primary and secondary spectra simultaneously for calibration transfer. The procedure of GA-IDS includes(1)Randomly generating chromosomes in the population;(2)Analyzing each one of the chromosomes and deleting the error ones; (3)Obtaining the primary and secondary spectra regions and the corresponding Root mean squared error of validation (RMSEV) based on each one of the chromosomes; (4)Executing selection, crossover and mutation operations. After finishing one loop, the GA-IDS goes to step (2) to repeat execute errors correction, RMSEV computation, selection, crossover and mutation operation. After achieving the criterion of the final termination, the spectra regions with minimal RMSEV can be retained. Two datasets, including corn and wheat datasets, were used to evaluate this algorithm. The results show that, compared with full variables, GA-IDS can select good regions for both primary and secondary spectra to reduce prediction errors. Compared with Iterative interval backward selection (IIBS), GA-IDS can achieve lower errors at the small size transfer set.
[1] Rios-Reina R, Callejon R M, Amigo J M. Food Control, 2021, 130: 108365.
[2] Dos Santos V J, Baqueta M R, Marco P H, et al. Food Chemistry, 2022, 368: 130675.
[3] Mishra P, Nikzad-Langerodi R. Talanta, 2021, 232: 122461.
[4] Li L, Huang W, Wang Z, et al. Postharvest Biology and Technology, 2022, 183: 111720.
[5] Rehman T U, Zhang L, Ma D, et al. Computers and Electronics in Agriculture, 2020, 176: 105685.
[6] Monakhova Y B, Diehl B W K. Journal of Chemometrics, 2020, 34(4): e3210.
[7] Wang X, Mao D Z, Yang Y J. Journal of Pharmaceutical and Biomedical Analysis, 2021, 194: 113766.
[8] Xu Z, Fan S, Cheng W, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 230: 118053.
[9] Too J, Abdullah A R. Journal of Supercomputing, 2021, 77(3): 2844.
[10] Katoch S, Chauhan S S, Kumar V. Multimedia Tools and Applications, 2021，80: 8091.
[11] Eliaerts J, Meert N, Dardenne P, et al. Talanta, 2020, 209: 120481.
[12] ZHENG Kai-yi, FENG Yu-hang, ZHANG Wen, et al(郑开逸, 冯雨航, 张 文，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(6): 1789.
[13] Giovenzana V, Beghi R, Parisi S，et al. Journal of the Science of Food and Agriculture, 2018, 98(5): 1935.
[14] Tugnolo A, Giovenzana V, Beghi R，et al. Computers and Electronics in Agriculture, 2021, 180: 105887.
